This study focuses on the design and implementation of a cost-effective smart home system, which leverages Internet of Things (IoT) technologies, low-power communication via LoRa, and data processing to enable automation and remote management of household devices and environmental conditions. The growing need for more efficient, secure, and energy-sustainable homes makes such systems particularly relevant today. An ESP32 microcontroller is used as the core of the system, to which various sensors are connected, including temperature, humidity, motion detection, gas leak detection, fire detection, and others. The ESP32 is responsible for collecting and processing the sensor data, which is then transmitted via LoRa (Long Range) technology to The Things Network (TTN). TTN serves as an intermediary gateway, forwarding the data to an online monitoring and visualization platform, Datacake. There, the data is displayed in real-time through graphs and charts, allowing users to remotely monitor the home environment. Simultaneously, the data is also sent to a Raspberry Pi, which functions as the central processing unit of the system. The Raspberry Pi runs a control algorithm that analyzes the sensor readings and issues commands to activate or deactivate connected devices. Examples of such automations include turning on or off lighting, heating, or ventilation systems, and notifying the residents in case of emergencies. The proposed system integrates cutting-edge technologies to develop a smart, interconnected, and autonomous home environment, offering enhanced comfort, safety, energy efficiency, and cost-effectiveness for the user. The system is low-cost, scalable, and adaptable to various settings and needs.

Security in academic laboratories involves not only the protection of physical resources but also the control of access to environments where research and equipment are concentrated. Conventional systems based on keys or access cards often present limitations related to loss, duplication, or lack of traceability. This project presents the development of an access control system based on facial recognition, designed for use in research laboratories. The solution integrates an ESP32-CAM module for image capture and facial identification, an ESP32 microcontroller to manage access locks, and a TFT display to show visual feedback. A web interface was implemented to register users and record access events, allowing monitoring of entry times and user identification. The development followed a structured approach: initial research and planning, definition of requirements, component selection, and staged implementation. Functionalities such as image processing, lock control, and access logging were progressively integrated and tested. The system was evaluated in a controlled environment simulating real laboratory access scenarios. It correctly recognized registered users, denied entry to unregistered individuals, and recorded access data without communication failures or delays. All components functioned as expected, including wireless data transmission and interface synchronization. The proposed system provides an alternative to traditional key-based methods by offering traceability, ease of use, and potential for replication in academic settings.

Falls are one of the leading causes of injury and loss of autonomy among the elderly, especially in the context of aging populations and the growing need for real-time health-monitoring technologies. This study develops and validates a low-cost wearable system that is designed to detect falls and monitor posture using embedded Internet of Things (IoT) infrastructure. The system architecture integrates an ESP32 microcontroller with an MPU6050 inertial sensor, wirelessly transmitting motion data via the MQTT protocol to a Raspberry Pi 3, which processes the information and activates an external camera when a fall is suspected. A threshold-based algorithm was implemented to classify user postures and detect abrupt motion variations associated with fall events. The entire system was validated through controlled experiments simulating daily activities—such as standing, walking, sitting, and lying down—as well as various types of falls. The results indicated reliable performance in detecting upright and supine postures and capturing acceleration and angular velocity patterns during simulated falls. However, the system presented difficulties in distinguishing sitting from fall events and identifying soft falls, achieving an overall classification accuracy of 60%. Hardware integration, wireless communication stability, and real-time visualization through the Node-RED dashboard were implemented, highlighting the feasibility of combining embedded sensing with lightweight communication protocols for wearable elderly-monitoring applications.

Nonlinearity in the transfer function of analog-to-digital converters (ADCs) is a major factor contributing to measurement errors in digital signal processing systems. While ideal ADCs produce a linear mapping between input voltage and digital output code, real devices introduce deviations that distort the spectral composition of the signal. Manufacturers typically specify only integral (INL) and differential (DNL) nonlinearity parameters, which provide limited insight into the practical impact of these imperfections.

This study presents a modeling approach for estimating the influence of ADC nonlinearity on the amplitude characteristics of digital signal spectra. Based on known values of INL and DNL, analytical relationships are used to simulate the appearance of additional harmonics and the deviation of the fundamental spectral component, without requiring experimental reconstruction of the actual ADC transfer function. Both sinusoidal and polyharmonic input signals are considered to reflect realistic measurement conditions.

Simulation results show that even moderate nonlinearity levels can lead to measurable spectral distortion, which may compromise signal integrity in precision applications. The developed approach provides a practical framework for evaluating the suitability of ADCs in spectral measurement systems and helps anticipate performance limitations before hardware implementation.

The findings can support engineers and designers in optimizing ADC selection and mitigating error sources in digital measurement devices, particularly in fields requiring high spectral accuracy, such as instrumentation, communications, and signal diagnostics.

Access to reliable, real-time water quality analysis in remote regions is essential for environmental monitoring and public health. Traditional spectrophotometric methods, while accurate, are expensive and not suitable for in situ use, especially in low-resource contexts. This research addresses the lack of affordable portable instrumentation by proposing the development of a rapid, tailored, and low-cost optical spectrometer capable of characterizing water samples directly at the collection site. The main objective of this work was to design and implement a portable optical system using accessible components and open-source technology for the optical analysis of water from rural sources in Ecuador. A descriptive and experimental methodology was followed, encompassing three phases: theoretical analysis of optical systems, simulation of optical and electronic subsystems using FreeCAD and Proteus, and the physical construction of a working prototype with 3D-printed parts and a custom PCB. The system integrates a white LED, plano-convex lenses, adiffraction grating, a 3DU33 phototransistor, and an Arduino Nano with Bluetooth transmission. Water samples from four different sources were tested and compared with a commercial spectrophotometer (DR-2010). The prototype showed good agreement in the visible range with an acceptable standard deviation, making it a viable and economical alternative for field-based water quality monitoring. Future enhancements will focus on improving calibration accuracy and optical resolution.

In modern network environments, the configuration of services such as FTP servers remains a time-intensive task, particularly when performed manually. This research addresses the challenge of reducing configuration time and minimizing errors through the implementation of an automated provisioning system using open-source tools. The main problem identified is the inefficiency and risk of misconfiguration associated with manual setups of FTP servers, especially in organizations lacking specialized personnel. The objective of this study is to develop a provisioning system capable of automating the configuration of FTP services to reduce deployment time and improve reliability. Our methodology utilized an experimental approach in a virtualized environment using VirtualBox and AlmaLinux. Three automation tools—Shell Script, Chef, and Ansible—were used to configure identical FTP servers. A sample of 245 configuration processes was analyzed using statistical techniques, including the Kolmogorov–Smirnov normality test and the Kruskal–Wallis non-parametric test, implemented in R. The results demonstrate that automated provisioning significantly reduces configuration time from 34.56 minutes manually to 2.15 minutes with Ansible (93.78% reduction), 3.49 minutes with Shell Script (89.90%), and 11.22 minutes with Chef (67.53%). Each tool performed the full configuration process, including user creation, firewall rules, and service setup. In conclusion, the implementation of automated provisioning tools markedly improves efficiency in server deployment, with Ansible offering the best performance. This contributes to Infrastructure as Code practices and supports scalable, error-reduced network service administration.

The increasing digitalization of power substations and the integration of communication networks in electrical systems have exposed critical infrastructure to a growing number of cybersecurity threats. This study presents the design and implementation of a simulated environment to emulate cyberattacks—specifically Denial of Service (DoS) attacks—on digital substations, aiming to raise awareness and improve cybersecurity practices in operational technology (OT) networks. Using open-source tools such as Mininet, Wireshark, and hping3, a digital twin of a substation communication system was built to replicate realistic network behavior under both normal and attack conditions. During the attack scenario, SYN flood packets were sent at a rate of approximately 530 packets per second and sustained for 10 seconds, leading to a total of 5300 attack packets. Packet capture analysis revealed that the average packet size increased from 78 bytes (normal traffic) to over 110 bytes during the attack. Additionally, the number of TCP SYN packets increased by 90%, significantly disrupting normal communication flow. These anomalies were clearly observable in the time-sequence visualizations generated using Wireshark. The simulation demonstrated the vulnerability of digital substations to basic network-layer exploits. This emulated environment provides a valuable platform for educational and training purposes, allowing security practitioners and engineers to visualize and understand attack patterns in critical systems. This study emphasizes the importance of proactive cyber-defense strategies in modern power systems and proposes further integration with intrusion detection mechanisms and AI-based anomaly detection tools to enhance resilience.

Electrical substations play a critical role in energy distribution, where operational failures can lead to service interruptions and increased maintenance costs. The modernization of their monitoring systems is essential, especially in environments that demand reliability and real-time diagnostics. The monitoring of medium-voltage substations traditionally relies on local manual readings and on-site operator interventions, limiting real-time visibility and increasing operational delays. This study presents the development and implementation of a remote monitoring and control system for a medium-voltage substation using Internet of Things (IoT) infrastructure and the Node-RED platform. The objective is to enable real-time supervision and reduce the need for physical presence, improving operational efficiency and safety. The system architecture integrates smart meters, a compact programmable controller (Wago CC100), and industrial communication via the Modbus RTU protocol. The Node-RED environment, combined with FlowFuse Dashboard 2, was used to develop a human–machine interface (HMI) accessible through web browsers. The dashboard displays electrical parameters such as voltage, current, and power, and allows for remote switching operations. System development followed four stages: requirement analysis, communication infrastructure setup, HMI development, and validation testing. Functional tests demonstrated reliable data acquisition and consistent remote control response, validating the system’s performance under simulated operational conditions. The implemented solution highlights the potential of low-code IoT platforms in modernizing legacy electrical infrastructures and aligns with the principles of Industry 4.0 by integrating cyber–physical systems for enhanced supervision, predictive maintenance, and decision-making support in electrical networks.

Water is a finite and essential resource, and its efficient use is critical, especially in agriculture, which accounts for over 70% of global freshwater consumption. Rural microentrepreneurs often face difficulties accessing efficient irrigation technologies due to high costs and operational complexity. This study presents the development of a low-cost automated irrigation system aimed at supporting small-scale farmers, combining the principles of Industry 4.0 with embedded systems and Internet of Things (IoT) technologies. The proposed system integrates an ESP32 microcontroller with wireless communication and a set of environmental sensors—soil moisture, air humidity, temperature, and rainfall. The firmware was developed in C/C++ using the Arduino IDE, and the system communicates through the MQTT protocol to enable real-time monitoring and control via a supervisory interface. The chosen irrigation method was localized drip irrigation, installed in a passion fruit plantation (Passiflora edulis), due to its efficiency in reducing water waste. A functional prototype was implemented and validated under field conditions, successfully triggering irrigation events based on predefined environmental thresholds. The results indicate that the system operates autonomously and effectively, enabling water savings and better crop management without requiring constant human intervention. The project demonstrates that accessible technological solutions can enhance productivity and sustainability in rural settings, promoting digital inclusion and more efficient use of natural resources in agriculture.

The rapid evolution of smart cities demands highly efficient, intelligent, and adaptive sensing networks to optimize urban infrastructure, environmental monitoring, and resource management. Conventional IoT sensor networks often suffer from limitations such as high-power consumption, restricted sensitivity, and inefficient data processing. This research presents a metasurface-enabled AIoT (Artificial Intelligence of Things) sensor framework, integrating reconfigurable metasurfaces with AI-driven analytics to enhance urban sensing, communication, and automation. Metasurfaces, engineered nanostructures capable of manipulating electromagnetic waves, enable ultra-sensitive, programmable, and energy-efficient sensors. These sensors adapt to environmental changes dynamically, offering superior real-time data acquisition for applications such as air quality monitoring, structural health assessment, intelligent traffic management, and smart grid optimization. Integrating AI-based edge computing enhances real-time data processing, reducing latency and computational overhead, ensuring seamless operation even in high-density urban environments. Furthermore, the proposed architecture leverages deep learning models and predictive analytics to facilitate anomaly detection, early warning systems, and autonomous decision-making for urban sustainability. A novel hybrid computational framework combining metamaterial physics with deep neural networks is developed, demonstrating superior sensing accuracy, reconfigurability, and resilience compared to conventional IoT sensors. Experimental simulations and prototype validations confirm the system’s scalability, robustness, and real-world feasibility. By addressing key challenges in sensor adaptability, efficiency, and AI-driven automation, this research establishes a transformative approach to smart city development, ensuring sustainable, resilient, and intelligent urban ecosystems.